housing <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/landdata-states.csv")
head(housing[1:5])
##   State region    Date Home.Value Structure.Cost
## 1    AK   West 2010.25     224952         160599
## 2    AK   West 2010.50     225511         160252
## 3    AK   West 2009.75     225820         163791
## 4    AK   West 2010.00     224994         161787
## 5    AK   West 2008.00     234590         155400
## 6    AK   West 2008.25     233714         157458
hist(housing$Home.Value)

library(ggplot2)
ggplot(housing, aes(x = Home.Value)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

plot(Home.Value ~ Date,
     data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
       data=subset(housing, State == "TX"))
legend(1975, 400000,
       c("MA", "TX"), title="State",
       col=c("black", "red"),
       pch=c(1, 1))

ggplot(subset(housing, State %in% c("MA", "TX")),
       aes(x=Date,
           y=Home.Value,
           color=State))+
  geom_point()

hp2001Q1 <- subset(housing, Date == 2001.25) 
ggplot(hp2001Q1,
       aes(y = Structure.Cost, x = Land.Value)) +
  geom_point()

ggplot(hp2001Q1,
       aes(y = Structure.Cost, x = log(Land.Value))) +
  geom_point()

hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))

p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))

p1 + geom_point(aes(color = Home.Value)) +
  geom_line(aes(y = pred.SC))

p1 +
  geom_point(aes(color = Home.Value)) +
  geom_smooth()
## `geom_smooth()` using method = 'loess'

p1 + 
  geom_text(aes(label=State), size = 3)

# install.packages("ggrepel") 
library("ggrepel")
p1 + 
  geom_point() + 
  geom_text_repel(aes(label=State), size = 3)

p1 +
  geom_point(aes(size = 2),
             color="red") 

p1 +
  geom_point(aes(color=Home.Value, shape = region))
## Warning: Removed 1 rows containing missing values (geom_point).

# Exercise 1
library(ggplot2)
dat <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)
##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA
## 4 4      Angola      148 0.486 2.0               SSA
## 5 5   Argentina       45 0.797 3.0          Americas
## 6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
plot1 <- ggplot(dat, aes(x = CPI, y = HDI))
plot1 + geom_point(aes(color = Region, size = HDI.Rank))

args(geom_histogram)
## function (mapping = NULL, data = NULL, stat = "bin", position = "stack", 
##     ..., binwidth = NULL, bins = NULL, na.rm = FALSE, show.legend = NA, 
##     inherit.aes = TRUE) 
## NULL
args(stat_bin)
## function (mapping = NULL, data = NULL, geom = "bar", position = "stack", 
##     ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL, 
##     breaks = NULL, closed = c("right", "left"), pad = FALSE, 
##     na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) 
## NULL
p2 <- ggplot(housing, aes(x = Home.Value))
p2 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p2 + geom_histogram(stat = "bin", binwidth=4000)

housing.sum <- aggregate(housing["Home.Value"], housing["State"], FUN=mean)
rbind(head(housing.sum), tail(housing.sum))
##    State Home.Value
## 1     AK  147385.14
## 2     AL   92545.22
## 3     AR   82076.84
## 4     AZ  140755.59
## 5     CA  282808.08
## 6     CO  158175.99
## 46    VA  155391.44
## 47    VT  132394.60
## 48    WA  178522.58
## 49    WI  108359.45
## 50    WV   77161.71
## 51    WY  122897.25
ggplot(housing.sum, aes(x=State, y=Home.Value)) + 
  geom_bar(stat="identity")

# Exercise 2
library(ggplot2)
dat <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)
##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA
## 4 4      Angola      148 0.486 2.0               SSA
## 5 5   Argentina       45 0.797 3.0          Americas
## 6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_smooth(method = "lm")

# Exercise 2 Continued

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_line(stat = "smooth", method = "loess")

# Exercise 2 Bonus

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_smooth(span = .4)
## `geom_smooth()` using method = 'loess'

p3 <- ggplot(housing,
             aes(x = State,
                 y = Home.Price.Index)) + 
        theme(legend.position="top",
              axis.text=element_text(size = 6))
(p4 <- p3 + geom_point(aes(color = Date),
                       alpha = 0.5,
                       size = 1.5,
                       position = position_jitter(width = 0.25, height = 0)))

p4 + scale_x_discrete(name="State Abbreviation") +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"))

p4 +
  scale_x_discrete(name="State Abbreviation") +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"),
                         low = "blue", high = "red")

p4 +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"),
                         low = "blue", high = "red")

p4 +
  scale_color_gradient2(name="",
                        breaks = c(1976, 1994, 2013),
                        labels = c("'76", "'94", "'13"),
                        low = "blue",
                        high = "red",
                        mid = "gray60",
                        midpoint = 1994)

# Exercise 3

ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point() +
scale_x_continuous(name = "Corruption Perception Index") +
scale_y_continuous(name = "Human Development Index") +
  scale_color_manual(name = "Region of the world",
                     values = c("red"))

p5 <- ggplot(housing, aes(x = Date, y = Home.Value))
p5 + geom_line(aes(color = State))

(p5 <- p5 + geom_line() +
   facet_wrap(~State, ncol = 10))

p5 + theme_linedraw()

p5 + theme_light()

p5 + theme_minimal() +
  theme(text = element_text(color = "turquoise"))

theme_new <- theme_bw() +
  theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
        text=element_text(size = 12, family = "Serif", color = "ivory"),
        axis.text.y = element_text(colour = "purple"),
        axis.text.x = element_text(colour = "red"),
        panel.background = element_rect(fill = "pink"),
        strip.background = element_rect(fill = "orange"))

p5 + theme_new

housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data = housing, mean)
ggplot(housing.byyear,
       aes(x=Date)) +
  geom_line(aes(y=Home.Value), color="red") +
  geom_line(aes(y=Land.Value), color="blue")

library(tidyr)
home.land.byyear <- gather(housing.byyear,
                           value = "value",
                           key = "type",
                           Home.Value, Land.Value)
ggplot(home.land.byyear,
       aes(x=Date,
           y=value,
           color=type)) +
  geom_line()

dat <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/EconomistData.csv")

# Build inital plot
pc1 <- ggplot(dat, aes(x = CPI, y = HDI, color = Region))
pc1 + geom_point()

# Add in the trendline
(pc2 <- pc1 +
   geom_smooth(aes(group = 1),
               method = "lm",
               formula = y ~ log(x),
               se = FALSE,
               color = "red")) +
   geom_point()

# Make adjustments
pc2 +
  geom_point(shape = 1, size = 4)

(pc3 <- pc2 + geom_point(shape = 1, size = 2.5, stroke = 1.25))

# Label the points on the graph
pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
                   "Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
                   "India", "Italy", "China", "South Africa", "Spane",
                   "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                   "United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
                   "New Zealand", "Singapore")

(pc4 <- pc3 +
  geom_text(aes(label = Country),
            color = "gray20",
            data = subset(dat, Country %in% pointsToLabel)))

library("ggrepel")
(pc4 <- pc3 +
   geom_text_repel(aes(label = Country),
                   color = "gray20",
                   data = subset(dat, Country %in% pointsToLabel),
                   force = 10))

# Label the countries, etc.
dat$Region <- factor(dat$Region,
                     levels = c("EU W. Europe",
                                "Americas",
                                "Asia Pacific",
                                "East EU Cemt Asia",
                                "MENA",
                                "SSA"),
                     labels = c("OECD",
                                "Americas",
                                "Asia &\nOceania",
                                "Central &\nEastern Europe",
                                "Middle East &\nnorth Africa",
                                "Sub-Saharan\nAfrica"))
pc4$data <- dat
pc4

library(grid)
(pc5 <- pc4 +
  scale_x_continuous(name = "Corruption Perceptions Index, 2011 (10=least corrupt)",
                     limits = c(.9, 10.5),
                     breaks = 1:10) +
  scale_y_continuous(name = "Human Development Index, 2011 (1=Best)",
                     limits = c(0.2, 1.0),
                     breaks = seq(0.2, 1.0, by = 0.1)) +
  scale_color_manual(name = "",
                     values = c("deepskyblue4",
                                "deepskyblue3",
                                "deepskyblue2",
                                "darkcyan",
                                "brown3",
                                "brown4")) +
  ggtitle("Corruption and Human development"))

library(grid) 
(pc6 <- pc5 +
  theme_minimal() + 
  theme(text = element_text(color = "gray20"),
        legend.position = c("top"),  
        legend.direction = "horizontal",
        legend.justification = 0.1, 
        legend.text = element_text(size = 11, color = "gray10"),
        axis.text = element_text(face = "italic"),
        axis.title.x = element_text(vjust = -1), 
        axis.title.y = element_text(vjust = 2), 
        axis.ticks.y = element_blank(), 
        axis.line = element_line(color = "gray40", size = 0.5),
        axis.line.y = element_blank(),
        panel.grid.major = element_line(color = "gray50", size = 0.5),
        panel.grid.major.x = element_blank()
        ))